Table 1 Use of mosquito nets among women at different levels of independent variables
From: Individual and contextual correlates of mosquito net use among women in Nigeria
Variable | Used mosquito nets | |||
|---|---|---|---|---|
No | Yes | Total | p-value | |
N (%) | N (%) | N (%) | ||
Individual-level factors | 2860 (47.3) | 3188 (52.7) | 6048 (100.0) | |
Age | ||||
15–24 | 1061 (47.8) | 1161 (52.2) | 2222 (100.0) | |
25–34 | 1027 (46.5) | 1180 (53.5) | 2207 (100.0) | |
35+ | 772 (47.7) | 847 (52.3) | 1619 (100.0) | 0.672 |
Education | ||||
No education | 944 (39.5) | 1446 (60.5) | 2390 (100.0) | |
Primary | 423 (43.7) | 546 (56.3) | 969 (100.0) | |
Secondary/higher | 1493 (55.5) | 1196 (44.5) | 2689 (100.0) | < 0.001 |
Household wealth index | ||||
Poor | 731 (36.2) | 1286 (63.8) | 2017 (100.0) | |
Middle | 881 (43.7) | 1134 (56.3) | 2015 (100.0) | |
Rich | 1248 (61.9) | 768 (38.1) | 2016 (100.0) | < 0.001 |
Mosquito causes malaria | ||||
No | 948 (70.6) | 394 (29.4) | 1342 (100.0) | |
Yes | 1912 (40.6) | 2794 (59.4) | 4706 (100.0) | < 0.001 |
Exposed to malaria messages | ||||
No | 1951 (51.4) | 1846 (48.6) | 3797 (100.0) | |
Yes | 909 (40.4) | 1342 (59.6) | 2251 (100.0) | < 0.001 |
Chances of getting malaria are the same | ||||
No | 1745 (55.2) | 1414 (44.8) | 3159 (100.0) | |
Yes | 1115 (38.6) | 1774 (61.4) | 2889 (100.0) | < 0.001 |
Drugs for preventing malaria in pregnancy are effective | ||||
No | 892 (74.6) | 304 (25.4) | 1196 (100.0) | |
Yes | 1968 (40.6) | 2884 (59.4) | 4852 (100.0) | < 0.001 |
Tests are a good way to detect malaria | ||||
No | 823 (75.8) | 263 (24.2) | 1086 (100.0) | |
Yes | 2037 (41.1) | 2925 (58.9) | 4962 (100.0) | < 0.001 |
ACT is effective in treating malaria | ||||
No | 1250 (60.7) | 811 (39.3) | 2061 (100.0) | |
Yes | 1610 (40.4) | 2377 (59.6) | 3987 (100.0) | < 0.001 |
Number of household members | ||||
< 5 | 817 (48.6) | 864 (51.4) | 1681 (100.0) | |
5+ | 2043 (46.8) | 2324 (53.2) | 4367 (100.0) | 0.204 |
Community-level factors | ||||
Residence | ||||
Urban | 1276 (55.1) | 1041 (44.9) | 2317 (100.0) | |
Rural | 1584 (42.5) | 2147 (57.5) | 3731 (100.0) | < 0.001 |
Region | ||||
North Central | 454 (47.1) | 509 (52.9) | 963 (100.0) | |
Northeast | 446 (36.4) | 781 (63.6) | 1227 (100.0) | |
Northwest | 636 (38.4) | 1.019 (61.6) | 1655 (100.0) | |
Southeast | 434 (64.9) | 235 (35.1) | 669 (100.0) | |
South-South | 433 (54.0) | 369 (46.0) | 802 (100.0) | |
Southwest | 457 (62.4) | 275 (37.6) | 732 (100.0) | < 0.001 |
Socioeconomic disadvantage | ||||
Tertile 1 (least disadvantaged) | 1295 (63.3) | 752 (36.7) | 2047 (100.0) | |
Tertile 2 | 847 (42.3) | 1156 (57.7) | 2003 (100.0) | |
Tertile 3 (most disadvantaged) | 718 (35.9) | 1280 (64.1) | 1998 (100.0) | < 0.001 |
State-level factors | ||||
Socioeconomic disadvantage | ||||
Tertile 1 (least disadvantaged) | 1309 (62.8) | 774 (37.2) | 2083 (100.0) | |
Tertile 2 | 903 (44.9) | 1110 (55.1) | 2013 (100.0) | |
Tertile 3 (most disadvantaged) | 648 (33.2) | 1304 (66.8) | 1952 (100.0) | < 0.001 |